Skip to yearly menu bar Skip to main content


AutoAD III: The Prequel – Back to the Pixels

Tengda Han · Max Bain · Arsha Nagrani · Gül Varol · Weidi Xie · Andrew Zisserman

Arch 4A-E Poster #345
[ ]
Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT


Generating Audio Description (AD) for movies is a challenging task that requires fine-grained visual understanding and an awareness of the characters and their names. Currently, visual language models for AD generation are limited by a lack of suitable training data, and also their evaluation is hampered by using performance measures not specialized to the AD domain. In this paper, we make three contributions: (i) We propose two approaches for constructing AD datasets with aligned video data, and build training and evaluation datasets using these. These datasets will be publicly released; (ii) We develop a Q-former-based architecture which ingests raw video and generates AD, using frozen pre-trained visual encoders and large language models; and (iii) We provide new evaluation metrics to benchmark AD quality that are well matched to human performance. Taken together, we improve the state of the art on AD generation.

Live content is unavailable. Log in and register to view live content